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1. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 03 | Mar 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1413
Network Intrusion Detection System using Machine Learning
Prof. Vrushali V. Kondhalkar1, Swapnil Shende2, Abhishek Patwari3, Pranav Ovhal4
1Prof. Vrushali V. Kondhalkar, Dept. of Computer Engineering, JSCOE, Pune, Maharashtra, India
2Swapnil Shende, Dept. of Computer Engineering, JSCOE, Pune, Maharashtra, India
3Abhishek Patwari, Dept. of Computer Engineering, JSCOE, Pune, Maharashtra, India
4Pranav Ovhal, Dept. of Computer Engineering, JSCOE, Pune, Maharashtra, India
Department of Computer Engineering, Jayawantrao Sawant College of Engineering, Pune
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Abstract - Machine Learning Algorithms-based "Network
Intrusion Detection System" is a soft device that monitors
computer networks to detect malicious activity that is
intended to steal confidential information or damage / hack
network agreements. The technologies used in today's IDS
cannot deal with Dynamic Complex types. security Cyber
Attack on Computer Networks Login activity is primarily
based on accuracy.
The accuracy of the input should reducefalsealarmsand
increase the availability of alarms. To improve
performance, various techniques have been used in recent
activities. Analyze large network traffic data is the main
function of the access system.
A well-planned separation method is needed to
overcome this problem. Machine learning method such as
Vector Machine support (SVM)andNaive barsusedtotestIDS.
NSL-KDD information usingacquisition dataset, accuracyand
distortion level is calculated.
Key Words: NIDS, Machine Learning, KDD
1.INTRODUCTION
To detect unusual malicious activity on a network or system
by attackers Through Machine learning ”In today's world
one of the most dangerous things in the security of com
puter or network security is illegal access to a computer
system. As network applications are growing rapidly, new
types of network attacks are on the rise. To manage
suspicious activities our system needs to be reformed. Once
an attack is identified or unusual behavior is detected, a
notification can be sent to the director's strator. Access
access systems (IDS) take the path based on the network or
host by detecting and diverting attacks. In any case, these
products are subject to attack signatures often indicate a
malicious or suspicious intent. When the ID is checked these
patterns in network traffic are then derived from the
network.
Most of the techniques used in modern IDs they
cannot manage the flexible and complex environment of
Internet attacks on computer networks. So, effectively again
using adaptive mechanismsthatleadtohighdetectionlevels,
low false alarm levels. Methods of machine learning provide
appropriate accounting and communication costs.Matching
pattern is fast enough to check the presence of a signature in
the order of the incoming package and acquires a malicious
behavior and must meet both your expansion signature
number and link speed. There are several ways to use IDS.
This study describes the behavior of the machine
learning to identify intruders. This is very helpful in
preventing interference with some kind of related attack.
The model can also reach real timeidentification entrybased
on size reduction and simple separator.
This study aims to increase focus on a number of points:
• selecting the appropriate algorithm for the appropriate
tasks depending on the data types, size and network
behavior and requirements.
• Implement a well-developed development process by
preparing and selecting benchmark data set to build a
promising NIDS system.
• Data analysis, acquisition, modeling, and engineering key
features, are used several processing techniques by putting
them together in a smart order for best accuracy with low
data representation size and size.
• propose integration and a complete development process
using these algorithms and strategies from database
selection to testing algorithms used a different metric. What
can be expanded to other NIDS applications are governedby
an Intellectual body and they select the most suitable paper
for publishing after a thorough analysis of submitted paper.
Selected paper get published (online and printed) in their
periodicals and get indexed by number of sources.
1.1 Background and Related Work
IntegratingmachinelearningalgorithmsintoSDN
has attracted significant attention . A solution was solved to
solve the problems in the KDD Cup 99 by making a plan
extensive exploratory research, using the NSL-KDD data set
to achieve excellent accuracy entry discovery. The
experimental study was performed on five well-known and
well-performing individuals machine learning algorithms

2. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 03 | Mar 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1414
(RF, J48, SVM, CART, and Naïve Bayes). Relationships the
feature selection algorithm was used to reduce the
complexity of the features, which led to Only 13 features in
the NSL-KDD database.
A dynamic model of the "Intelligent Access
Acquisition System" proposed based on a specific AI method
of access acquisition. Strategies that integrate neural
networks and abstract thinking have a networkprofile,using
simple data mining techniques to process network data. The
program includes confusing, abuse and host-based
acquisitions. Simple comprehensive rulesallowforrulesthat
reflect common ways to define security attacks. There have
been a number of methods used by machine learning
applications to address the problem of selecting features for
access. In the author used PCA to identify space features in
the main character area and select features that correspond
to high eigen values using the Genetic Algorithm.. The same
models were re-trained using 13 reduced featurestoachieve
an average accuracy of 98%, 85%, 95%, 86%, and 73% in
each model. A deep neural network model was proposed to
detect and detect SDN intrusion.
1.2 Algorithms
1)Support Vector Machine:-
The Vector Support Machine (SVM) machine falls
under a supervised learning method, where different types
of data are trained from different subjects. In the upperpart,
SVM creates multiple hyperplanes or hyperplanes. A
hyperplane that properly separates data assigned to
different categories over a wide range, is considered a
leading aircraft. To test genes between hyperplanes, the
indirect detector uses a variety of kernel functions. Genetic
enhancement among hyperplanes is the main goal of these
kernel functions such as linear, polynomial, radial basis,and
sigmoid. Due to the growing attention in SVMs, outstanding
applications have been developed by developers and
researchers. SVM plays a major role in image processingand
pattern recognition applications.
2)Naive Bayes:-
Bayesian classifiers are statistical classifiers.They
are capable to forecast the probability that whether the
given model fits to a particular class. It is based on Bayes’
theorem. It constructed on the hypothesis that, for a given
class, the attribute value is independent to the values of the
attributes. This theory is called class conditional
independence.
2. Literature Survey
1. Ahmad et al [1] analyze well-known machine learning
methods namely. support vector method and advanced
reading machine. NSL datasetanddataminingdataaretaken
to assess the detection method. In their analysis results, it
was concluded that ELM is more accurate than RF, SVM in
whole data samples, and SVM is more accurate in part
samples, except that in quarterly data SVM is better.
Advanced Intelligent Access Acquisition System Using
Machine Learning 2178 Published Blue Eyes Intelligence
Engineering and Copy Recovery Science Number:
H6932068819 / 19 © BEIESP DOI: 10.35940 /
ijitee.H6932.078919
2. M. Al-Qatfet al [4] proposed an IDS approach thatusesself-
study (STL) which is an effective in-depth learning
technology for learning feature and size. This is done using a
scant auto encoder device which is a great way to learn how
to rearrange the drawing of a novel feature in an
uncontrolled manner. The paper currently improves the
accuracy of SVM sections as well as training times and test
times quickly. In addition, it produces accurate statistics in
two categories and five categories. The highest level of
precision in the five-phase division is achieved in this way
compared to other shallow subdivisions such as J48, Naive
Bayesian, RF, and SVM.
3. C. Xu et al [5] introduced the IDS reading comprehension
study using an output feature that enhances the in-depth
learning model. Proposed interventions that include a
continuousneuralsystemconsistingofduplicateunits(GRU),
multilayer perceptron (MLP), softmaxmodule. The study is
based on both the KDD database and the NSL-KDD data sets.
This paper concludes that the effect of BGRU and MLP in
combination with KDD 19 and NSL-KDD data is better.
4. Naseer et al [6] investigateda suitableIDS-basedapproach
that is based on a variety of deep emotional networks such
as convolutional neural systems, autoencoders,andperiodic
sensory systems. These were competent in the NSLKDD
database and rated on the NSLKDDTest + and
NSLKDDTest21 and operated on a GPU-based test bed using
the non-backed Keras. In this case, the test was performed
using organizational metrics namely. receiver performance
indicator, curve area, memory recall curve, intermediate
accuracy and deep separation precision and standard
machine learning methods.
3. FUTURE SCOPE
Future work deals with large volume of data, a
hybrid multi-level model will be constructed to improve the
accuracy.
It deals with building anmoreeffectivemodel based
on well. It deals with building an more effectivemodel based

3. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 03 | Mar 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 1415
on well- organised classifiers whicharecapabletocategorise
new attacks with better performance.
4. SYSTEM ARCHITECTURE
5. CONCLUSIONS
IDS is designed to provide basic acquisition strategies to
protect existing systems on networks directly or indirectly
online. But finally at the end of the day to the Network
Administrator to make sure his network is out of danger.
Many different methods have been used in the screening
process. Among them machine learning plays a vital role.
This analysis works with machine learning algorithms such
as KNN, DTC and Naïve Bayes.
This does not completely protect the network from
attackers, but IDS helps the Network Administrator track
down the bad guys on the internet whose purposeistobring
your network into a hotspot and make it vulnerable to
attack.
REFERENCES
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